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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 2	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 3	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
The CEO of a large multi-sector organization has learned that, in the absence of formal guidance, business units have created shadow IT by integrating AI into their daily operations. In response, an AI governance group has been created to address the issue. Which of the following is MOST important for the group to collect from each business unit?

A. Business continuity plan (BCP)
B. API key management practices
C. Inventory of models in use
D. Software bill of materials

Answer: C

Explanation:
Establishing a comprehensive inventory of AI models in use provides immediate visibility into shadow IT, enabling the governance group to understand scope, assess risks, and prioritize controls. This foundational step is necessary before addressing operational practices or implementing governance measures.

NEW QUESTION # 300
Which of the following BEST strengthens information security controls around the use of generative AI applications?

A. Implementing a kill switch
B. Ensuring controls exceed industry benchmarks
C. Validating AI model training data
D. Monitoring AI outputs against policy

Answer: D

Explanation:
AAISM identifies continuous monitoring of AI outputs--especially generative outputs--as the most effective security control, ensuring that violations, unsafe responses, data leakage, and policy- breaking behavior are detected and corrected.

NEW QUESTION # 301
When developing organizational generative AI policies, the FIRST step is to:

A. identify potential risk associated with the deployment of generative AI.
B. inventory the current state of AI systems, use cases, and processes.
C. adopt current AI legal and regulatory requirements.
D. understand how the organization intends to ethically use generative AI.

Answer: B

Explanation:
Inventorying the current state of AI systems, use cases, and processes is the first step because an organization must understand where and how generative AI is being used before it can effectively define governance, assess risks, establish ethical expectations, or apply regulatory requirements.

NEW QUESTION # 302
A global organization has experienced multiple incidents of staff copying confidential data into public chatbots and acting on the model outputs. Which of the following is MOST important to reduce short-term risk when launching an AI security awareness initiative?

A. Requiring employees to complete an annual generic phishing and deepfake awareness module
B. Blocking access to public large language models (LLMs) at the network perimeter
C. Delivering role-based and scenario-driven AI security training mapped to policy and job functions
D. Publishing an AI acceptable use policy and collecting e-signatures of employees

Answer: C

Explanation:
AAISM prescribes targeted, role-based, scenario-driven training aligned to policy and job tasks as the highest-impact near-term intervention for human-factor AI risks. By mapping concrete "do/don't" behaviors (e.g., what data may/may not be pasted into public chatbots, required redaction steps, approved tools, verification of outputs) to specific roles, organizations rapidly reduce incident likelihood and harmful actions.
* A (blocking) is a technical containment option but is not an awareness-initiative control and may cause workarounds; AAISM treats it as complementary, not a substitute for behavior change.
* B generic modules fail to address the specific misuse pattern.
* D signatures provide attestations without ensuring comprehension or changed behavior.
References:* AI Security Management (AAISM) Body of Knowledge: Human-centric Controls-Role- based training, policy-to-practice mapping, and scenario exercises for rapid risk reduction.* AI Security Management Study Guide: Awareness program design for generative AI misuse; behavior-anchored training outcomes.

NEW QUESTION # 303
The PRIMARY objective of performing tests to detect membership inference in machine learning (ML) models is to determine whether the model:

A. does not exhibit any biases toward certain groups of data.
B. can handle changes in the input data without affecting its accuracy.
C. is susceptible to attacks that alter predictions through adversarial inputs.
D. can reveal details about the data it was trained on.

Answer: D

Explanation:
Membership inference testing evaluates whether a model can disclose whether specific data points were part of its training dataset, indicating potential leakage of sensitive information about the data it was trained on.

NEW QUESTION # 304
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